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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/python-machine-learning
课程评论：没有评论
课程名称：Python中的应用机器学习 课程概述：本课程旨在向学习者介绍应用机器学习，强调技术和方法而非统计原理。课程将首先讨论机器学习与描述性统计的区别，并通过教程介绍scikit-learn工具包。将讨论数据的维度问题、数据聚类及其评估方法。课程还将描述创建预测模型的监督学习方法，学习者将能够应用scikit-learn的预测建模方法，并理解与数据可推广性相关的过程问题（例如交叉验证、过拟合）。课程最后将探讨更高级的技术，如集成模型的构建及预测模型的实际限制。完成本课程后，学生将能够识别监督（分类）和无监督（聚类）技术的区别，确定为特定数据集及需求应应用的技术，工程特征以满足该需求，并编写Python代码进行分析。 课程前置要求：建议在参加本课程之前，完成Python中的数据科学入门和Python中的应用绘图、图表及数据表示课程，之后可以学习Python中的应用文本挖掘和Python中的应用社会分析课程。 课程大纲： 1. 模块1：机器学习基础 - SciKit Learn简介 - 介绍基本的机器学习概念、任务和工作流程，以基于K近邻法的分类问题为例，并使用scikit-learn库实现。 2. 模块2：监督机器学习 - 第1部分 - 深入了解多种监督学习方法，涵盖分类和回归，学习模型复杂性与泛化性能之间的关系、特征缩放的重要性，以及通过应用正则化等技术来控制模型复杂度以避免过拟合。本周内容包括K近邻、线性回归（最小二乘法、岭回归、套索回归、多项式回归）、逻辑回归、支持向量机、用于模型评估的交叉验证及决策树。 3. 模块3：评估 - 探讨评估和模型选择方法，帮助理解和优化机器学习模型的性能。 4. 模块4：监督机器学习 - 第2部分 - 涉及更先进的监督学习方法，包括树的集成（随机森林、梯度提升树）和神经网络（包括可选的深度学习总结）。学习数据泄漏的关键问题及其检测和避免方法。
Name:Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn
Description:This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library.
Name:Module 2: Supervised Machine Learning - Part 1
Description:This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.
Name:Module 3: Evaluation
Description:This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.
Name:Module 4: Supervised Machine Learning - Part 2
Description:This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
